Distinguishing enzymes using metabolome data for the hybrid dynamic/static method

<p>Abstract</p> <p>Background</p> <p>In the process of constructing a dynamic model of a metabolic pathway, a large number of parameters such as kinetic constants and initial metabolite concentrations are required. However, in many cases, experimental determination of t...

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Main Authors: Nakayama Yoichi, Ishii Nobuyoshi, Tomita Masaru
Format: Article
Language:English
Published: BMC 2007-05-01
Series:Theoretical Biology and Medical Modelling
Online Access:http://www.tbiomed.com/content/4/1/19
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spelling doaj-c841fa3ddbe143fea9bc26744b9944412020-11-25T00:42:23ZengBMCTheoretical Biology and Medical Modelling1742-46822007-05-01411910.1186/1742-4682-4-19Distinguishing enzymes using metabolome data for the hybrid dynamic/static methodNakayama YoichiIshii NobuyoshiTomita Masaru<p>Abstract</p> <p>Background</p> <p>In the process of constructing a dynamic model of a metabolic pathway, a large number of parameters such as kinetic constants and initial metabolite concentrations are required. However, in many cases, experimental determination of these parameters is time-consuming. Therefore, for large-scale modelling, it is essential to develop a method that requires few experimental parameters. The hybrid dynamic/static (HDS) method is a combination of the conventional kinetic representation and metabolic flux analysis (MFA). Since no kinetic information is required in the static module, which consists of MFA, the HDS method may dramatically reduce the number of required parameters. However, no adequate method for developing a hybrid model from experimental data has been proposed.</p> <p>Results</p> <p>In this study, we develop a method for constructing hybrid models based on metabolome data. The method discriminates enzymes into static modules and dynamic modules using metabolite concentration time series data. Enzyme reaction rate time series were estimated from the metabolite concentration time series data and used to distinguish enzymes optimally for the dynamic and static modules. The method was applied to build hybrid models of two microbial central-carbon metabolism systems using simulation results from their dynamic models.</p> <p>Conclusion</p> <p>A protocol to build a hybrid model using metabolome data and a minimal number of kinetic parameters has been developed. The proposed method was successfully applied to the strictly regulated central-carbon metabolism system, demonstrating the practical use of the HDS method, which is designed for computer modelling of metabolic systems.</p> http://www.tbiomed.com/content/4/1/19
collection DOAJ
language English
format Article
sources DOAJ
author Nakayama Yoichi
Ishii Nobuyoshi
Tomita Masaru
spellingShingle Nakayama Yoichi
Ishii Nobuyoshi
Tomita Masaru
Distinguishing enzymes using metabolome data for the hybrid dynamic/static method
Theoretical Biology and Medical Modelling
author_facet Nakayama Yoichi
Ishii Nobuyoshi
Tomita Masaru
author_sort Nakayama Yoichi
title Distinguishing enzymes using metabolome data for the hybrid dynamic/static method
title_short Distinguishing enzymes using metabolome data for the hybrid dynamic/static method
title_full Distinguishing enzymes using metabolome data for the hybrid dynamic/static method
title_fullStr Distinguishing enzymes using metabolome data for the hybrid dynamic/static method
title_full_unstemmed Distinguishing enzymes using metabolome data for the hybrid dynamic/static method
title_sort distinguishing enzymes using metabolome data for the hybrid dynamic/static method
publisher BMC
series Theoretical Biology and Medical Modelling
issn 1742-4682
publishDate 2007-05-01
description <p>Abstract</p> <p>Background</p> <p>In the process of constructing a dynamic model of a metabolic pathway, a large number of parameters such as kinetic constants and initial metabolite concentrations are required. However, in many cases, experimental determination of these parameters is time-consuming. Therefore, for large-scale modelling, it is essential to develop a method that requires few experimental parameters. The hybrid dynamic/static (HDS) method is a combination of the conventional kinetic representation and metabolic flux analysis (MFA). Since no kinetic information is required in the static module, which consists of MFA, the HDS method may dramatically reduce the number of required parameters. However, no adequate method for developing a hybrid model from experimental data has been proposed.</p> <p>Results</p> <p>In this study, we develop a method for constructing hybrid models based on metabolome data. The method discriminates enzymes into static modules and dynamic modules using metabolite concentration time series data. Enzyme reaction rate time series were estimated from the metabolite concentration time series data and used to distinguish enzymes optimally for the dynamic and static modules. The method was applied to build hybrid models of two microbial central-carbon metabolism systems using simulation results from their dynamic models.</p> <p>Conclusion</p> <p>A protocol to build a hybrid model using metabolome data and a minimal number of kinetic parameters has been developed. The proposed method was successfully applied to the strictly regulated central-carbon metabolism system, demonstrating the practical use of the HDS method, which is designed for computer modelling of metabolic systems.</p>
url http://www.tbiomed.com/content/4/1/19
work_keys_str_mv AT nakayamayoichi distinguishingenzymesusingmetabolomedataforthehybriddynamicstaticmethod
AT ishiinobuyoshi distinguishingenzymesusingmetabolomedataforthehybriddynamicstaticmethod
AT tomitamasaru distinguishingenzymesusingmetabolomedataforthehybriddynamicstaticmethod
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